Recognises a rule assigned to a missing data quality check
enforcement: dq-check and no DQ rule cites it. The rule has quietly stopped existing.
Can AI do this?
People still lead Few or none of the tested AI models showed they master this. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 68% | Not shown |
| gemini-3.1-pro-preview* | Flagship | 99% | Mastered |
| gpt-6-astra* | Flagship | 100% | Mastered |
| claude-fable-5-1* | Flagship | 100% | Mastered |
| gemini-3.8-flash* | Mid tier | 100% | Mastered |
| gpt-6.1-sol* | Mid tier | 100% | Mastered |
| claude-sonnet-5-5* | Mid tier | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 49% | Not shown |
| gpt-6-luna | Low cost | 57% | Not shown |
| claude-haiku-5-5 | Low cost | 62% | Not shown |
* From the earlier benchmark of 2026-10: 10 data-modelling test tasks, 3 runs per task, graded by claude-opus-5-5. These models have not taken the current 123 tasks yet; the two methods differ, so compare their figures with care. They are left out of the “most models” counts and the best-model picks.
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: The rule and the check to build
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: A temporal rule marked dq-check with an empty dq-rules.yaml Acceptable: A DQ-nnn entry citing the rule, with a dimension, an expression and a threshold (ANO-007, DQR-007)
Also known as: Rule assigned to a check that does not exist (data rule); Rule assigned to a check that does not exist (data quality rule)
Related knobits
Builds on
All 23 knobits of Document data rules in SBVR.
Source: Review checklist 02-data-rules (data-quality), row 12; formerly data-modelling K-312